A hybrid meta-heuristic algorithm for optimization of crew scheduling

Crew scheduling problem is the problem of assigning crew members to the flights so that total cost is minimized while regulatory and legal restrictions are satisfied. The crew scheduling is an NP-hard constrained combinatorial optimization problem and hence, it cannot be exactly solved in a reasonable computational time. This paper presents a particle swarm optimization (PSO) algorithm synchronized with a local search heuristic for solving the crew scheduling problem. Recent studies use genetic algorithm (GA) or ant colony optimization (ACO) to solve large scale crew scheduling problems. Furthermore, two other hybrid algorithms based on GA and ACO algorithms have been developed to solve the problem. Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm over other algorithms.     

Multi-directional local search for a bi-objective dial-a-ride problem in patient transportation

This paper considers a generalization of a bi-objective dial-a-ride problem, incorporating real-life characteristics of patient transportation. It studies the impact of combination restrictions, preventing particular user combinations and limiting the set of drivers to which particular users can be assigned. The academic literature currently lacks insights into the effect of these restrictions on the cost structure of a service provider. A multi-directional local search algorithm is developed to solve this problem, taking into account the fundamental tradeoff between operational efficiency and service quality. Local search is integrated into a variable neighborhood descent framework that applies an intelligent candidate list principle to reduce computation time. Moreover, a new scheduling procedure is proposed, constructing time schedules that minimize total user ride time. It proves to be faster and more efficient than existing scheduling procedures. Overall, computational experiments on existing benchmark data extended with combination restrictions reveal a general pattern in the effect of the combination restrictions. Such insights are essential for service providers in order to support policy choices, e.g. related to service quality or medical education of drivers.     

模拟退火算法在带约束的送货路线优化设计中的应用

意在解决送货路线优化设计问题,即在给定送货点和其他一些约束的条件下,确定所最优的运行路线,使所用时间最少。通过将设计最优送货路线的问题转换成图论中的旅行商的问题来求解。其中,对于问题一,限定各送货点的送货时间,求解此问题需在一般模型的基础上添加时间约束来构建新的求解模型;而对于问题二来说,其没有时间限制,但其货物的总重...     

基于复杂网络理论及蚁群算法的MA迁移策略

移动Agent问题主要是解决移动Agent在不同主机间移动时如何根据移动Agent的任务和其他约束条件来规划最优的迁移路线。蚁群算法是一种新的生物进化算法,具有并行、正反馈和启发式搜索等特点,是一种解决旅行Agent问题的有效手段,但同时也存在一些缺点,如运算过程中收敛速度慢,易出现停滞现象等。复杂网络理论是一个新兴的理论,它发现现实的网络具有新的特性,为了刻画这一新的网络结构,引入了新的特征度量,节点的“度”就是其中一个。在蚁群算法的基础上,在状态转移规则等中加入度这一系数,同时自适应调整挥发系数ρ来提高算法的性能。将该算法用于移动Agent问题,模拟计算结果显示移动Agent在移动时能以最优的效率和最短的时间来完成迁移。     

Multi-objective scheduling and a resource allocation problem in hospitals

This study addresses the issue of scheduling medical treatments for resident patients in a hospital. Schedules are made daily according to the restrictions on medical equipment and physicians who are being assigned at the same time. The problem is formulated as a multi-objective binary integer programming (BIP) model. Three types of metaheuristics are proposed and implemented to deal with the discrete search space, numerous variables, constraints and multiple objectives: a variable neighborhood search (VNS)-based method, scatter search (SS)-based methods and a non-dominated sorting genetic algorithm (NSGA-II). This paper also provides the results of computational experiments and compares their ability to find efficient solutions to the multi-objective scheduling problem.     

Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms

This paper presents the use of genetic algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ventilating and air conditioning systems concerning energy performance and indoor comfort requirements. This problem has some specific restrictions that make it very particular and complex because of the large time requirements existing due to the need of considering multiple criteria (which enlarges the solution search space) and to the long computation time models require to assess the accuracy of each individual.To solve these restrictions, a genetic tuning strategy considering an efficient multicriteria approach has been proposed. Several fuzzy logic controllers have been produced and tested in laboratory experiments in order to check the adequacy of such control and tuning technique. To do so, accurate models of the controlled buildings (two real test sites) have been provided by experts. Finally, simulations and real experiments were compared determining the effectiveness of the proposed strategy.     

A framework for the application of metaheuristics to?tasks-to-processors assignation problems

The optimization of the execution time of a parallel algorithm can be achieved through the use of an analytical cost model function representing the running time. Typically the cost function includes a set of parameters that model the behavior of the system and the algorithm. In order to reach an optimal execution, some of these parameters must be fitted according to the input problem and to the target architecture. An optimization problem can be stated where the modeled execution time for the algorithm is used to estimate the parameters. Due to the large number of variable parameters in the model, analytical minimization techniques are discarded. Exhaustive search techniques can be used to solve the optimization problem, but when the number of parameters or the size of the computational system increases, the method is impracticable due to time restrictions. The use of approximation methods to guide the search is also an alternative. However, the dependence on the algorithm modeled and the bad quality of the solutions as a result of the presence of many local optima values in the objective functions are also drawbacks to these techniques. The problem becomes particularly difficult in complex systems hosting a large number of heterogeneous processors solving non-trivial scientific applications. The use of metaheuristics allows for the development of valid approaches to solve general problems with a large number of parameters. A well-known advantage of metaheuristic methods is the ability to obtain high-quality solutions at low running times while maintaining generality. We propose combining the parameterized analytical cost model function and metaheuristic minimization methods, which contributes to a novel real alternative to minimize the parallel execution time in complex systems. The success of the proposed approach is shown with two different algorithmic schemes on parallel heterogeneous systems. Furthermore, the development of a general framework allows us to easily develop and experiment with different metaheuristics to adjust them to particular problems.     

A state space analysis of propose-and-revise

A central research topic in the area of knowledge engineering is the reuse of problem-solving methods for developing knowledge based systems. For being able to reuse a problem-solving method it is important to know under which restrictions a problem-solving method is appropriate to solve a given problem. This paper describes the problem-solving method propose-and-revise as well as the way this problem-solving method searches in its problem space for a solution. A quantitative analysis of the efficiency of this search process is given. Additionally, task and domain specific properties and restrictions and their influence on the efficiency of the search process are considered. For these purposes an instance of the problem-solving method is transformed to a corresponding instance of a Stanford Research Institute Problem Solver (STRIPS) planning system. Then the problem-solving method is considered as an additional control strategy for such a planning system. By this way the various insights and analysis results which are available in the area of planning systems may be exploited for the analysis of problem-solving methods. ©1999 John Wiley & Sons, Inc.     

Finding good nurse duty schedules: a case study

Constructing duty schedules for nurses at large hospitals is a difficult problem. The objective is usually to ensure that there is always sufficient staff on duty, while taking into account individual preferences with respect to work patterns, requests for leave and financial restrictions, in such a way that all employees are treated fairly. The problem is typically solved via mixed integer programming or heuristic (local) search methods in the operations research literature. In this paper the problem is solved using a tabu search approach as a case study at Stikland Hospital, a large psychiatric hospital in the South African Western Cape, for which a computerized decision support system with respect to nurse scheduling was developed. This decision support system, called NuRoDSS (short for Nurse Rostering Decision Support System) is described in some detail.     

基于Memetic算法的要地防空优化部署方法

火力单元优化部署问题是网络化防空火控系统的一个重要研究内容. 本文将要地防空优化部署作为组合优化问题, 优化目标为最大化部署方案对保护要地的防御贡献程度, 约束主要考虑了地理条件和火力资源. 利用网格离散化思想对防区进行划分, 对部署方案、火力覆盖能力、约束条件以及火力覆盖要求等条件进行了表征, 建立了问题的数学模型. 构造了一种基于Memetic算法的优化求解方法, 运用遗传算法和邻域搜索作为全局和局部搜索方法, 用解的构造方式和选择策略处理了约束条件,比较了局部搜索使用不同邻域时算法的运行效率. 最后通过实验验证了本方法的合理性和有效性.     

蚁群算法在移动Agent迁移中的应用研究

移动Agent提供了一种全新的分布计算范型 .移动Agent技术给分布式系统的设计、实现和维护都带来了新的活力 .旅行Agent问题是一类复杂的组合优化问题,目的在于解决移动Agent在不同主机间移动时如何根据移动Agent的任务和其他约束条件来规划最优的迁移路线 .蚁群算法作为一种新的生物进化算法,具有并行、正反馈和启发式搜索等特点,是一种解决旅行Agent问题的有效手段,受到了广泛的关注,但它与其他进化算法一样存在易陷入局部最小的缺点 .在蚁群算法的基础上,通过修改它的信息素轨迹更新规则,引入自适应的信息素挥发系数来提高收敛速度和算法的全局最优解搜索能力,从而使得移动Agent在移动时以最优的效率和最短的时间来完成迁移 .仿真结果表明,改进的算法在解的性能和收敛速度上均优于相关算法 .     

A multicriteria ant colony algorithm for generating music playlists

In this paper we address the problem of music playlist generation based on the user-personalized specification of context information. We propose a generic semantic multicriteria ant colony algorithm capable of dealing with domain-specific problems by the use of ontologies. It also employs any associated metadata defined in the search space to feed its solution-building process and considers any restrictions the user may have specified. An example is given of the use of the algorithm for the problem of automatic generation of music playlists, some experimental results are presented and the behavior of the approach is explained in different situations.     

Computational improvement of dynamic programming solutions by multiprocessing techniques

The potential use of multiprocessing computers for possible improvement of dynamic programming solutions is considered. In particular, the dimensionality restrictions and the search in case of a multidimensional control vector are discussed. While the dimension of a practically solvable problem would be increased only slightly, a considerable improvement could be expected in case of a parallel search for a multidimensional control vector.     

旅行商问题的人工智能算法应用研究

本文是用人工智能中的深度优先算法、宽度优先算法、A*算法、SMA*算法来解决N城市旅行商问题。通过解决路径、耗散值、运行时间等数据比较这几个算法在处理该问题上的优劣。     

Metaheuristic algorithms for solving two interconnected vehicle routing problems in a hospital complex

This paper addresses a real-life logistic problem arising in the hospital complex of Tours (France). The two-level vehicle routing problem has time windows, a heterogeneous fleet, and multi-depot, multi-commodity and split deliveries. The first level concerns the routing problem for a fleet of vehicles serving several hospital units that delivers medicines, clean linen, meals, various supplies, patient files and picks up waste and dirty linen. The second level concerns the problem of routing employees between buildings within a large hospital unit. Both levels are interconnected. In addition, decisions about sizing and planning the teams of drivers and warehouse employees have to be made. Two metaheuristic algorithms are proposed to solve the entire problem: a genetic algorithm and a tabu search. The algorithms are tested on 100 instances, randomly generated on the basis of real-life instances.     

Crew pairing optimization based on hybrid approaches

The crew pairing problem (CPP) deals with generating crew pairings due to law and restrictions and selecting a set of crew pairings with minimal cost that covers all the flight legs. In this study, we present three different algorithms to solve CPP. The knowledge based random algorithm (KBRA) and the hybrid algorithm (HA) both combine heuristics and exact methods. While KBRA generates a reduced solution space by using the knowledge received from the past, HA starts to generate a reduced search space including high quality legal pairings by using some mechanisms in components of genetic algorithm (GA). Zero-one integer programming model of the set covering problem (SCP) which is an NP-hard problem is then used to select the minimal cost pairings among solutions in the reduced search space. Column generation (CG) which is the most commonly used technique in the CPP literature is used as the third solution technique. While the master problem is formulated as SCP, legal pairings are generated in the pricing problem by solving a shortest path problem on a structured network. In addition, the performance of CG integrated by KBRA (CG_KBRA) and HA (CG_HA) is investigated on randomly generated test problems. Computational results show that HA and CG_HA can be considered as effective and efficient solution algorithms for solving CPP in terms of the computational cost and solution quality.     

一种求解供应链环境下生产-配送问题的集成优化方法

研究了供应链环境下的生产.配送集成优化问题,从整体的角度优化需求分配、生产调度、配送拼装和车辆调度,利用交货时间因素来协调各模块的优化过程,进面得到更优的运作方案.设计了一个禁忌搜索和遗传算法相结合的集成优化策略,对两个不同规模的问题分别进行了独立优化和集成优化,数值实验结果显示丫集成优化策略的优越性.最后通过对惩罚因...     

新颖的全局和声搜索算法在经济性调度中的应用

提出采用新颖的全局和声搜索算法来解决经济调度问题,并设计了一种新颖的处理系统约束的方法;介绍了经济调度问题数学模型、新颖的全局和声搜索算法实现过程及其应用方法。实验结果表明,采用新颖的全局和声搜索算法所获得的最优值要明显好于采用进化算法、粒子群算法所获得的最优值,新颖的全局和声搜索算法为解决经济性调度问题提供了一种新的解决方案。     

Genetic algorithm-based heuristics for an open shop scheduling problem with setup,processing, and removal times separated

This report proposes a solution to the open shop scheduling problem with the objective of minimizing total job tardiness in the system. Some practical processing restrictions, such as independent setup and dependent removal times, are taken into account as well. The addressed problem is first described as a 0–1 integer programming model, and is then solved optimally. Subsequently, some hybrid genetic-based heuristics are proposed to solve the problem in an acceptable computation time. To demonstrate the adaptability of these heuristics, some performance comparisons are made with solutions provided by running either a mathematical programming model or certain classic meta-heuristics such as genetic algorithm, simulated annealing, and tabu search in various manufacturing scenarios. The experimental results show that the hybrid genetic-based heuristics perform well, especially the DGA. However, these heuristics require some more additional computations but are still acceptable.     

Feature selection using tabu search method

Selecting an optimal subset from original large feature set in the design of pattern classifier is an important and difficult problem. In this paper, we use tabu search to solve this feature selection problem and compare it with classic algorithms, such as sequential methods, branch and bound method, etc., and most other suboptimal methods proposed recently, such as genetic algorithm and sequential forward (backward) floating search methods. Based on the results of experiments, tabu search is shown to be a promising tool for feature selection in respect of the quality of obtained feature subset and computation efficiency. The effects of parameters in tabu search are also analyzed by experiments.